Responsibilities
- Conceive, design, engineer, and implement ML and AI solutions by studying information needs; conferring with users; studying systems flow, data usage, and work processes; investigating problem areas
- Routinely demonstrate initiative and creativity in developing technology solutions
- Serve as technical expert or lead projects/programs and technical staff to develop, test and implement significant new products, or operational improvements or devise new approaches to problems at the division/business unit
- Mentor junior engineers
- Design secure, scalable, and compliant architectures for AI/ML, generative AI, and agentic AI solutions for finance use cases
- Select and integrate AWS services for data pipelines, model hosting, vector search, orchestration, monitoring, and governance
- Establish deployment standards, CI/CD, observability, and cost controls
- Build and deploy predictive analytics, ML, and gen AI solutions into production
- Develop robust data/model pipelines, APIs, and integration layers
- Implement MLOps practices for training, validation, deployment, and monitoring
- Support model performance, retraining, testing, and production support
- Contribute to agentic workflows, tool use, and orchestration patterns
- Select and integrate AWS services for data pipelines, model hosting, vector search, orchestration, monitoring, and governance
- Highly autonomous and productive in performing activities, requiring only minimal direction from or interaction with supervisor
- Accountable for total project scope, budget, completion within budget constraints and scheduled completion date
- Accountable for successful and timely completion of all tasks/projects under direct and matrix control
- Maintains relationships with affiliates, subsidiaries, and vendors in accordance with AbbVie Values, Vendor Management Office, and Purchasing to further the mission, vision, and goals of the organization
- Understand and adhere to corporate standards regarding applicable Corporate and Divisional Policies, including code of conduct, safety, GxP compliance, data security, and the software development lifecycle
Requirements
- Design and develop machine learning and AI solutions for financial use cases
- Lead technical architecture for both traditional and deep learning models
- Execute traditional machine learning solutions
- Develop and integrate generative AI and agentic AI technologies
- Automate workflows, enhance decision-making, and enable intelligent systems within financial applications
- Contribute to AI strategy
- Collaborate cross-functionally with stakeholders (e.g., risk, compliance, business units)
- Evaluate emerging AI/ML technologies
- Accountability for AI system auditability
- Design secure, scalable, and compliant architectures for AI/ML, generative AI, and agentic AI solutions
- Select and integrate AWS services for data pipelines, model hosting, vector search, orchestration, monitoring, and governance
- Establish deployment standards, CI/CD, observability, and cost controls
- Build and deploy predictive analytics, ML, and gen AI solutions into production
- Develop robust data/model pipelines, APIs, and integration layers
- Implement MLOps practices for training, validation, deployment, and monitoring
- Support model performance, retraining, testing, and production support
- Contribute to agentic workflows, tool use, and orchestration patterns
- Highly autonomous and productive in performing activities, requiring only minimal direction from or interaction with supervisor
- Accountable for total project scope, budget, completion within budget constraints and scheduled completion date
- Accountable for successful and timely completion of all tasks/projects under direct and matrix control
- Maintain relationships with affiliates, subsidiaries, and vendors in accordance with AbbVie Values, Vendor Management Office, and Purchasing to further the mission, vision, and goals of the organization
- Understand and adhere to corporate standards regarding applicable Corporate and Divisional Policies, including code of conduct, safety, GxP compliance, data security, and the software development lifecycle
